In [13]:
# Identify year columns (those that can be converted to integers)
year_columns = [col for col in df.columns if col != 'Country Name' and col.isdigit()]
years = list(map(int, year_columns)) # Convert to integers for plotting
# Create the figure
fig, axes = plt.subplots(1, 3, figsize=(20, 6), sharex=True)
# Plot Birth Rate
for country in df['Country Name'].unique():
country_data = df[df['Country Name'] == country]
values = country_data[year_columns].values.flatten()
axes[0].plot(years, values, label=country, alpha=0.7)
axes[0].set_xlabel('Year')
axes[0].set_ylabel('Birth Rate')
axes[0].set_title('Birth Rate Trends for All Countries')
axes[0].grid(True)
# Repeat for GDP
year_columns_gdp = [col for col in GDP.columns if col != 'Country Name' and col.isdigit()]
years_gdp = list(map(int, year_columns_gdp))
for country in GDP['Country Name'].unique():
country_data = GDP[GDP['Country Name'] == country]
values = country_data[year_columns_gdp].values.flatten()
axes[1].plot(years_gdp, values, label=country, alpha=0.7)
axes[1].set_xlabel('Year')
axes[1].set_ylabel('GDP')
axes[1].set_title('GDP Trends for All Countries')
axes[1].grid(True)
# Repeat for Unemployment
year_columns_unemp = [col for col in Unemployment.columns if col != 'Country Name' and col.isdigit()]
years_unemp = list(map(int, year_columns_unemp))
for country in Unemployment['Country Name'].unique():
country_data = Unemployment[Unemployment['Country Name'] == country]
values = country_data[year_columns_unemp].values.flatten()
axes[2].plot(years_unemp, values, label=country, alpha=0.7)
axes[2].set_xlabel('Year')
axes[2].set_ylabel('Unemployment Rate')
axes[2].set_title('Unemployment Rate Trends for All Countries')
axes[2].grid(True)
# Set consistent x-axis ticks
axes[2].set_xticks(years[::5])
axes[2].set_xticklabels(years[::5], rotation=0)
plt.setp(axes, xticks=years[::5], xticklabels=years[::5])
plt.tight_layout()
plt.show()
